Quantitative Horizon Scanning for Mitigating Technological Surprise: Detecting the Potential for Collaboration at the Interface

Authors:
Carey E. Priebe;Jeffrey L. Solka;David J. Marchette;Avory C. Bryant
Affiliations:
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218-2682, USA;Naval Surface Warfare Center, Dahlgren, VA, USA;Naval Surface Warfare Center, Dahlgren, VA, USA;Naval Surface Warfare Center, Dahlgren, VA, USA
Venue:
Statistical Analysis and Data Mining
Year:
2012

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Abstract

‘The identification of potential breakthroughs before they happen’ is a vague data analysis problem and ‘the scientific literature’ is a massive, complex dataset. Hence QHS for MTS might seem to be prototypical of the data miner's lament: ‘Here's some data we have… can you find something interesting?’ Nonetheless, the problem is real and important, and we develop an innovative statistical approach thereto—not a final etched-in-stone approach, but perhaps the first complete quantitative methodology explicitly addressing QHS for MTS. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining5: 178–186, 2012 (This article is based on a Keynote Address given by one author (C.E.P.) at QMDNS 2010, May 25–26, Fairfax, VA, USA (presentation slides available at...))